Lead the Data function across Machine Learning, Data Science, Analytics, and Data Engineering, setting technical direction, developing people, and strengthening data-driven decision making.
Locations (4)
Austin, Texas, United States
Illinois, United States
Texas, United States
Colorado, United States
Responsibilities
Directly manage, coach, and develop ML Engineers, Data Analysts, and Data Engineers
Shape hiring, develop technical leaders and qualified reviewers, and delegate decisions
Set a practical ML and data strategy with Technology leadership, Product, and business stakeholders
Hands-on engineering role focused on building, deploying, and operating AI systems in a regulated enterprise environment, specifically on AWS.
Compensation
Anticipated Hiring Range: $120,000 - $150,000 annual base salary depending on experience, qualifications, and geographic location. $130,000 - $170,000 annual base salary depending on experience, qualifications, and geographic location in CA, CT, MA, NJ, NY, and PA
Responsibilities
Design and implement end-to-end AI/ML solutions including LLM-based applications
Build RAG pipelines using vector databases and enterprise data sources
Build machine learning models that automate their training, validation, monitoring, and retraining
Develop APIs and services to operationalize AI capabilities across the organization
Build ingestion for multimodal content and transformation pipelines for structured and unstructured data
Lead the design, development, and deployment of AI models and algorithms; collaborate with cross‑functional teams; analyze complex data sets; implement and optimize AI solutions; provide technical guidance and mentorship; stay current with AI trends.
Responsibilities
Lead the design, development, and deployment of AI models and algorithms
Collaborate with cross‑functional teams to define project goals and requirements
Analyze complex data sets to identify trends and patterns using statistical and machine learning approaches
Implement and optimize AI solutions to improve data accuracy, efficiency, and predictive capabilities
Provide technical guidance and mentorship to junior data scientists
Support digital transformation programs for national and international clients, helping them value data, processes, and applications through cloud-native technologies, modern Data Platforms, Machine Learning, Generative AI, and Agentic AI.
Compensation
La fascia di retribuzione annua lorda prevista è compresa tra 33.058,74 € e 50.500 €
Responsibilities
Progettare e sviluppare piattaforme Data & AI su AWS adottando architetture cloud-native, lakehouse e data product-oriented.
Realizzare pipeline di data ingestion, ETL/ELT e integrazione dati provenienti da sistemi enterprise, applicazioni, API e fonti cloud.
Disegnare dataset, modelli dati, semantic layer e data product governati a supporto di Analytics, Machine Learning e Artificial Intelligence.
Implementare soluzioni basate su Amazon SageMaker, AWS Glue, Lake Formation e servizi AWS dedicati alla gestione del dato.
Sviluppare e industrializzare modelli di Machine Learning supportandone l'intero ciclo di vita, dalla sperimentazione fino alla messa in produzione.
Requirements
Diploma o Laurea in discipline tecnico-scientifiche
Nice to have: Amazon Bedrock
Nice to have: Amazon SageMaker AI
Nice to have: Amazon Q
Nice to have: RAG (Retrieval Augmented Generation)
Lead enterprise AI transformation initiatives for AWS customers, designing frameworks, driving adoption, and influencing product roadmaps.
Locations (3)
Seattle, Washington, United States
New York, New York, United States
Mountain View, California, United States
Compensation
USA, CA, Mountain View - 210,200.00 - 284,300.00 USD annually
USA, NY, New York - 201,000.00 - 272,000.00 USD annually
USA, WA, Seattle - 182,800.00 - 247,300.00 USD annually
Responsibilities
Design and lead comprehensive business execution transformation strategies for enterprise customers adopting Amazon Quick and AWS Gen AI services
Build repeatable transformation methodologies, maturity models, and assessment frameworks
Synthesize learnings into playbooks and best practices
Co‑develop transformation offerings with strategic consulting partners and support partner‑led enablement programs
Support enterprise AI/ML architecture initiatives, governance frameworks, and agentic AI capabilities across federal modernization programs.
Compensation
Salary Range: $120k - $155k
Responsibilities
Support AI/ML architecture design and engineering activities across enterprise architecture and modernization initiatives.
Assist in developing and maintaining enterprise AI architecture baselines, target-state architectures, and transition roadmaps.
Contribute to solution architecture efforts for AI-enabled systems, integrating cloud-native services and advanced analytics platforms.
Support the development and maintenance of AI model governance frameworks, including model lifecycle management, risk assessments, and compliance monitoring.
Assist in designing scalable data pipeline architectures to support AI/ML training, inference, monitoring, and continuous improvement.
Requirements
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
Master’s degree in Computer Science, Engineering, Information Technology, or related discipline
Nice to have: Master’s degree
Nice to have: AI/ML platforms and services including Azure AI, Azure Machine Learning, Amazon SageMaker, OpenAI services
Nice to have: Generative AI and large language model (LLM) technologies
The Senior AI Engineer will be part of the Global Data Science and Innovation team, building a world-class portfolio of AI-led solutions and productionizing R&D PoCs particularly using Generative AI.
Responsibilities
Develop and maintain scalable and efficient AI pipelines and infrastructure for deployment.
Deploy AI models and solutions into production environments, ensuring stability and performance.
Monitor and maintain deployed AI systems to ensure they operate effectively and efficiently.
Troubleshoot and resolve issues related to AI deployment, including performance bottlenecks and system failures.
Optimize deployment processes to reduce latency and improve the scalability of AI solutions.
Requirements
Advanced degree from top tier technical institutes in relevant discipline
Nice to have: experience developing/maintaining live deployments
The purpose of this role is to bridge the gap between complex data infrastructure and actionable business strategy, providing day-to-day support to Revenue Operations (RevOps) including Sales, Marketing, and Customer Experience leaders.
Compensation
The expected annual base salary range for this position is: $84,225/year to $140,375/year. This position is bonus eligible / commission-based.
Responsibilities
Lead the migration of legacy workflows from Alteryx to dbt, ensuring high-quality, documented, and tested data models that serve as a single source of truth.
Manage and monitor data pipelines using Dagster or Airflow to ensure reliability and performance within our Redshift warehouse.
Act as the primary liaison between technical data structures and non-technical leaders.
Translate complex 'data speak' into clear insights that highlight ROI and business impact.
Actively explore and implement AI/LLM capabilities to enhance data discovery and predictive modeling.
The role bridges the gap between complex data infrastructure and actionable business strategy, providing support to RevOps by migrating workflows from Alteryx to dbt and building data models.
Compensation
The expected annual base salary range for this position is: $84,225/year to $140,375/year. This position is bonus eligible / commission-based.
Responsibilities
Lead the migration of legacy workflows from Alteryx to dbt, ensuring high-quality, documented, and tested data models.
Manage and monitor data pipelines using Dagster or Airflow to ensure reliability and performance within our Redshift warehouse.
Act as the primary liaison between technical data structures and non-technical leaders.
Translate complex data speak into clear insights that highlight ROI and business impact.
Actively explore and implement AI/LLM capabilities to enhance data discovery and predictive modeling.
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